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Research Seminar Finrisk University of Zurich - 12/15/2006 The Dog That Did not Bark: Insider Trading and Crashes Jose M. Marin Universitat Pompeu Fabra and CREA Jacques Olivier HEC School of Management, GREGHEC and CEPR

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Research Seminar Finrisk University of Zurich - 12/15/2006 Motivation (1)  A famous quote from Sir Arthur Conan Doyle (« Silver Blaze »): ‘Is there any other point to which you would wish to draw my attention?’ ‘To the curious incident of the dog in the night- time’ ‘The dog did nothing in the night-time’ ‘That was the curious incident’ remarked Sherlock Holmes

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Research Seminar Finrisk University of Zurich - 12/15/2006 The Data (2)  Crash variables: –Constraint imposed by our model:  Has testable implications about when a crash occurs  Does not have testable implications about size of crash (because of multiple equilibria)  Thus, define crash variable as a 0/1 variable –Constraint imposed by regulation:  Prior to 2002, insider trade may be reported only month after trade  Thus, work at monthly frequencies –Constraint imposed by our model:  Makes sense at individual stock level  Makes less sense at the level of the market  Thus need to correct for market fluctuations

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Research Seminar Finrisk University of Zurich - 12/15/2006 The Data (3)  Crash variables (continued): –Thus, two alternative measures of crashes:  ERCRASH i,t = 1 if excess return of stock i in month t is more than 2 standard deviations away and below average excess return (computed over a 5-year rolling window)  MMCRASH i,t computed the same way as ERCRASH using 1- factor beta adjustment  Average threshold for a monthly return to be considered a crash: - 22%

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Research Seminar Finrisk University of Zurich - 12/15/2006 The Data (4)  Insider trading variables –INSSAL, INSPURCH and INSTV –Normalized by market capitalization of the stock at the close of the transaction day  Past returns –Included for two reasons:  Found to predict insider trading by existing literature  Found to predict negative skewness by existing literature  Total trading volume –Included for two reasons:  Found to predict negative skewness by existing literature  Want to make sure that insider trading is not a proxy for total trading volume

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Research Seminar Finrisk University of Zurich - 12/15/2006 Basic Results (1)  Preliminary regression: do crashes coincide with insiders getting into the market or out of the market ?

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Research Seminar Finrisk University of Zurich - 12/15/2006 Basic Results (2)  Leading regression: does the pattern of insider sales predict crashes the way suggested by the theory?

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Research Seminar Finrisk University of Zurich - 12/15/2006 Competing Stories (1)  Insiders trying to evade SEC scrutiny: –SEC investigates insider trades close to date of large stock market fluctuations –Insiders who do not want to see their trades investigated only exploit long-lived information –Thus selling by insiders today unlikely to coincide with crash in near future  Key factual element: SEC prosecutes at least as much insiders having purchased stocks before (positive) jumps as they do insiders having sold shares before crashes (e.g. Meulbroek, 1992)  Implication: –If the “evading SEC scrutiny” story is correct then pattern of insider purchases prior to (positive) jumps should be at least as strong as pattern of insider sales prior to crashes –If our story is correct, then pattern should disappear

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Research Seminar Finrisk University of Zurich - 12/15/2006 Competing Stories (2)  Is the pattern of insider purchases prior to jumps the same as pattern of insider sales prior to crashes?

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Research Seminar Finrisk University of Zurich - 12/15/2006 Competing Stories (3)  Result could be a pure artefact: –Many crashes occur on earning announcement dates –Insiders are not allowed to trade before earning announcement dates –Thus we observe in the data that times at which insiders have not traded also correspond to times at which crashes are more frequent  Implication: –If the “earning announcement date” story is correct then pattern of insider sales prior to crashes should disappear once we remove all observations corresponding to months where earning announcement occurred –If our story is correct, then the evidence should be at least as strong as in the entire sample

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Research Seminar Finrisk University of Zurich - 12/15/2006 Competing Stories (4)  What is the evidence in the subsample without earning announcement dates?

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Research Seminar Finrisk University of Zurich - 12/15/2006 Robustness Checks (2)  Definition of crash variable –Crashes defined using raw returns –One of two variables loses significance  Dividing the sample into two subsamples –Before and after 1996 –Stronger results after 1996  Window for past insider trading –6 months vs. 1 year vs. 2 years –Retain significance –2 years works slightly better than 1 year which works slightly better than 6 months

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Research Seminar Finrisk University of Zurich - 12/15/2006 Conclusions  Insiders get out of the market shortly before it crashes –Implication: crashes are periods of higher uncertainty  Pattern of insider sales help predict crashes –Implication: insider sales also contain information  Pattern of insider sales before crashes and pattern of insider purchases before jumps are different